Leaves Classification and Leaf Mass EstimationSummaryFor the first problem, we establish our neural network model to classify leaves of trees by taking eight characteristics of leaf into consideration. The eight characteristics consist of sawtooth number, petiole length, blade length, blade width, blade thickness, leaf area and circular degree. Our results are summarized in a conclusion that we classify leaves into fourteen types including linear, lanceolate, oblanceolate, spatulate, ovat, obovate, elliptic, oblong, deltoid, reniform, orbicular, peltate, perfoliate and connate. Our neural network implement the classification task reliably and correctly.For the second problem, we set up our AHP model to figure out the reasons why leaves have the various shapes and come to a conclusion that gene, auxin, climate and disease are the main reasons which lead to various shapes.For the third problem, we discuss this issue from the perspective of growth evolutionary and hormones, build cells mechanic model to solve this problem and sum up the conclusion that the shapes are inclined to minimize overlapping individual shadows that are cast so as to maximize exposure. The shape is effected by the distribution of leaves within the volume of the tree and its branches. For the fourth problem, we use statistical analysis knowledge to analyse the data among tree profiles, branching structure and leaf shapes, after mathematically analyzing, finally find that leaves shapes have a direct relation with the tree profile and branching structure,For the fifth problem, we formulate our volumetric method for leaf mass estimation and linear regression model for seeking and comparing the correlation between the leaf mass and tree heigh...
